Therapy Triggered by Predication of Disordered Breathing

ABSTRACT

An approach to providing disordered breathing therapy includes providing therapy based on a prediction of disordered breathing. One or more patient conditions are detected and used to predict disordered breathing. Therapy is delivered to mitigate the predicted disordered breathing. The disordered breathing therapy may be adapted to enhance therapy efficacy and/or to reduce the impact of the therapy to the patient.

RELATED PATENT DOCUMENTS

This is a divisional of U.S. patent application Ser. No. 10/643,154,filed on Aug. 18, 2003, to which Applicant claims priority under 35U.S.C. §120, and which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to providing therapy fordisordered breathing based on prediction of disordered breathing.

BACKGROUND OF THE INVENTION

Disordered breathing refers to a wide spectrum of respiratory conditionsthat involve disruption of the normal respiratory cycle. Althoughdisordered breathing typically occurs during sleep, the condition mayalso occur while the patient is awake. Respiratory disruption can beparticularly serious for patients concurrently suffering fromcardiovascular deficiencies, such as congestive heart failure.Unfortunately, disordered breathing is often undiagnosed. If leftuntreated, the effects of disordered breathing may result in serioushealth consequences for the patient.

Various types of disordered respiration have been identified, including,for example, apnea, hypopnea, dyspnea, hyperpnea, tachypnea, andperiodic breathing, including Cheyne-Stokes respiration (CSR). Apnea isa fairly common disorder characterized by periods of interruptedbreathing. Apnea is typically classified based on its etiology. One typeof apnea, denoted obstructive apnea, occurs when the patient's airway isobstructed by the collapse of soft tissue in the rear of the throat.Central apnea is caused by a derangement of the central nervous systemcontrol of respiration. The patient ceases to breathe when controlsignals from the brain to the respiratory muscles are absent orinterrupted. Mixed apnea is a combination of the central and obstructiveapnea types. Regardless of the type of apnea, people experiencing anapnea event stop breathing for a period of time. The cessation ofbreathing may occur repeatedly during sleep, sometimes hundreds of timesa night and sometimes for a minute or longer.

In addition to apnea, other types of disordered respiration have beenidentified, including hypopnea (shallow breathing), tachypnea (rapidbreathing), hyperpnea (heavy breathing), and dyspnea (laboredbreathing). Combinations of the respiratory cycles described above maybe observed, including, for example, periodic breathing andCheyne-Stokes breathing. Periodic breathing is characterized by cyclicrespiratory patterns that may exhibit rhythmic rises and falls in tidalvolume. Cheyne-Stokes respiration is a specific form of periodicbreathing wherein the tidal volume decreases to zero resulting in apneicintervals. The breathing interruptions of periodic breathing and CSR maybe associated with central apnea, or may be obstructive in nature. CSRis frequently observed in patients with congestive heart failure (CHF)and is associated with an increased risk of accelerated CHF progression.Because of the cardiovascular implications, therapy forrespiration-related sleep disorders is of particular interest.

SUMMARY OF THE INVENTION

Various embodiments of present invention involve methods and systems forproviding disordered breathing therapy based on prediction of disorderedbreathing.

In accordance with an embodiment of the invention, a method of providingtherapy for disordered breathing involves detecting one or moreconditions associated with disordered breathing and predictingdisordered breathing based on the one or more detected conditions.Therapy to mitigate the predicted disordered breathing is delivered. Atleast one of detecting the conditions, predicting the disorderedbreathing, and delivering the therapy is performed at least in partimplantably.

In accordance with a further embodiment of the invention, a method ofproviding disordered breathing therapy involves predicting disorderedbreathing and adapting a therapy to mitigate the disordered breathing.The adapted therapy is delivered to the patient. At least one ofpredicting the disordered breathing, adapting the therapy, anddelivering the adapted therapy is performed at least in partimplantably.

Yet another embodiment of the invention includes a medical device forproviding disordered breathing therapy. The medical device includes adetector system configured to detect one or more conditions associatedwith disordered breathing. A prediction engine is coupled to thedetector system and is configured to predict disordered breathing basedon the one or more detected conditions. A therapy delivery system iscoupled to the prediction engine and is configured to delivery therapyto the patient to mitigate the predicted disordered breathing.

A further embodiment of the invention involves a disordered breathingtherapy system including means for detecting one or more conditionsassociated with disordered breathing and means for predicting disorderedbreathing based on the detected conditions. The system further includesmeans for delivering therapy to mitigate the predicted disorderedbreathing. At least one of the means for detecting the one moreconditions, the means for predicting disordered breathing, and the meansfor delivering therapy includes an implantable component.

Yet another embodiment of the invention involves a system for providingtherapy for disordered breathing. The system includes means forpredicting disordered breathing and means for adapting a therapy tomitigate the predicted disordered breathing. The system further includesmeans for delivering the adapted therapy to the patient. At least one ofthe means for predicting disordered breathing, the means for adapting atherapy to mitigate the disordered breathing, and the means fordelivering the adapted therapy to the patient includes an implantablecomponent.

The above summary of the present invention is not intended to describeeach embodiment or every implementation of the present invention.Advantages and attainments, together with a more complete understandingof the invention, will become apparent and appreciated by referring tothe following detailed description and claims taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow graph illustrating a method for providing disorderedbreathing therapy in accordance with embodiments of the invention;

FIG. 2 is a block diagram of a disordered breathing therapy system inaccordance with embodiments of the invention;

FIG. 3 illustrates conditions that may be used to predict disorderedbreathing according to embodiments of the invention;

FIG. 4 is a block diagram of a disordered breathing prediction engine inaccordance with embodiments of the invention;

FIG. 5 is a flow graph illustrating a method of updating a predictioncriteria library according to embodiments of the invention;

FIG. 6 is a block diagram of a cardiac rhythm management systemincorporating a disordered breathing prediction engine in accordancewith embodiments of the invention;

FIG. 7 is a diagram illustrating a system for predicting disorderedbreathing in accordance with embodiments of the invention;

FIG. 8A illustrates a representative graph of tidal volume signal usedin connection with disordered breathing prediction in accordance withembodiments of the invention;

FIG. 8B illustrates a representative graph of heart rate signal used inconnection with disordered breathing prediction in accordance withembodiments of the invention;

FIG. 8C illustrates a representative graph of an activity signal used inconnection disordered breathing prediction in accordance withembodiments of the invention;

FIG. 9 is a flow graph of a method for delivering an adapted therapy fordisordered breathing in accordance with embodiments of the invention;

FIG. 10 illustrates adjustment of an activity sleep threshold using anMV condition in accordance with embodiments of the invention;

FIG. 11 illustrates a normal respiration pattern as represented by atransthoracic impedance sensor signal;

FIG. 12 illustrates respiration intervals used for disordered breathingdetection according to embodiments of the invention;

FIG. 13 illustrates detection of sleep apnea and severe sleep apneaaccording to embodiments of the invention;

FIGS. 14A-14B are graphs of tidal volume derived from transthoracicimpedance measurements according to embodiments of the invention;

FIG. 15 is a flow graph illustrating a method of apnea and hypopneadetection according to embodiments of the invention;

FIG. 16 is a graph illustrating a breathing interval according toembodiments of the invention;

FIG. 17 is a graph illustrating a hypopnea detection approach inaccordance with embodiments of the invention;

FIGS. 18A-18B are charts illustrating disordered breathing events thatcan be addressed in accordance with embodiments of the invention;

FIGS. 18C-18G are graphs illustrating disordered breathing eventscomprising a mixture of apnea and hypopnea respiration cycles;

FIG. 19 is a flow graph of a method for detecting disordered breathingby classifying breathing patterns in accordance with embodiments of theinvention;

FIG. 20 is a block diagram illustrating a system for adapting a therapyto mitigate disordered breathing based on therapy efficacy and minimalimpact to the patient; and

FIG. 21 is a flow graph of a method of adapting a therapy for disorderedbreathing using disordered breathing detection and sleep qualityassessment.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail below. It is to be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the invention isintended to cover all modifications, equivalents, and alternativesfalling within the scope of the invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

In the following description of the illustrated embodiments, referencesare made to the accompanying drawings which form a part hereof, and inwhich are shown by way of illustration, various embodiments by which theinvention may be practiced. It is to be understood that otherembodiments may be utilized, and structural and functional changes maybe made without departing from the scope of the present invention.

A significant percentage of patients between the ages of 30 and 60 yearsexperience some symptoms of disordered breathing. Sleep disorderedbreathing is associated with excessive daytime sleepiness, systemichypertension, increased risk of stroke, angina and myocardialinfarction. Disordered breathing is particularly prevalent amongcongestive heart failure patients, and may contribute to the progressionof heart failure.

Various therapies have been used to treat central and/or obstructivedisordered breathing episodes. Obstructive sleep apnea has beenassociated with prolapse of the tongue and its surrounding structureinto the pharynx, thus occluding the respiratory pathway. A commonlyprescribed treatment for obstructive apnea is continuous positive airwaypressure (CPAP). A CPAP device delivers air pressure through a nasalmask worn by the patient. The application of continuous positive airwaypressure keeps the patient's throat open, reducing or eliminating theobstruction causing apnea.

Prolapse of the tongue muscles has been attributed to diminishingneuromuscular activity of the upper airway. A treatment for obstructivesleep apnea involves compensating for the decreased muscle activity byelectrical activation of the tongue muscles. The hypoglossal (HG) nerveinnervates the protrusor and retractor tongue muscles. An appropriatelyapplied electrical stimulation to the hypoglossal nerve, for example,prevents backward movement of the tongue, thus preventing the tonguefrom obstructing the airway.

Cardiac stimulation may be used as a therapy for disordered breathing.Therapy methods using cardiac pacing is described in commonly owned U.S.Publication No. 2005/0039745, filed concurrently with this patentapplication, and incorporated by reference herein in its entirety. Thecardiac pacing method described uses an adaptive therapy based ondetection of disordered breathing. Such a disordered breathing therapymay be adapted, for example, to achieve an overall level of therapyefficacy, patient comfort, sleep quality, interaction with other patienttherapies, or device service life.

Embodiments of the invention discussed herein relate to systems andmethods providing an adaptive therapy for disordered breathing based onprediction of disordered breathing. Various approaches for predictingdisordered breathing are described in commonly owned U.S. Pat. No.7,396,333 and incorporated by reference herein in its entirety.

The flowchart of FIG. 1 illustrates a method for triggering disorderedbreathing therapy based on a prediction of disordered breathingaccording to various embodiments of the invention. The method involvesdetecting 110 conditions associated with disordered breathing andpredicting disordered breathing 120 based on the detected conditions.Disordered breathing may be predicted, for example, by comparing thedetected conditions to disordered breathing prediction criteria. Arepresentative set of conditions that may be used to predict disorderedbreathing are listed in Table 1. The representative set of conditionslisted in Table 1 is not exhaustive, and conditions other than thoselisted may be used to predict disordered breathing. If disorderedbreathing is predicted, therapy is delivered 130 to mitigate thedisordered breathing, e.g., reduce the severity of the disorderedbreathing or prevent the disordered breathing from occurring. One ormore of detecting the patient conditions, predicting the disorderedbreathing based on the detected patient conditions and delivering thetherapy to mitigate the disordered breathing is performed as least inpart implantably. Implantably performing an operation comprisesperforming the operation by a process that is performed at leastpartially within the patient's body, or by using a component, device, orsystem that is implantable within the patient's body.

Patient conditions used in the disordered breathing prediction may bephysiological or contextual (e.g. non-physiological). The physiologicalconditions may include a broad category of conditions associated withthe internal physiological conditions of the patient. Physiologicalconditions may be further subdivided, for example, into conditions ofthe cardiovascular, respiratory, and nervous systems, as well asconditions relating to the blood chemistry of the patient. In connectionwith the prediction of sleep disordered breathing, an additionalphysiological category associated with the patient's sleep quality mayalso be defined.

Contextual conditions generally encompass the external conditionsaffecting the patient. Contextual conditions may be broadly defined toinclude, for example, non-physiological environmental conditions such astemperature, humidity, air pollution index, ambient noise, andbarometric pressure as well as body-related conditions such as patientlocation, posture, and altitude. Contextual conditions may also includehistorical conditions relating to the patient, including the patient'snormal sleep time and the patient's medical history, for example.Methods and systems for detecting contextual conditions are described incommonly owned U.S. Pat. No. 7,400,928, and incorporated by referenceherein in its entirety. Methods and systems for REM sleep detection aredescribed in commonly owned U.S. Publication No. 2005/0043652,incorporated herein by reference.

Table 1 provides a representative set of patient conditions that may beused in connection with prediction of disordered breathing, along withexample sensing methods that may be employed to detect the conditions.

TABLE 1 Sensor type or Detection Condition Type Condition methodPhysiological Cardiovascular System Heart rate EGM, ECG Heart ratevariability QT interval Ventricular filling pressure Intracardiacpressure sensor Blood pressure Blood pressure sensor Respiratory SystemSnoring Accelerometer Microphone Respiration pattern Transthoracicimpedance (Tidal volume Minute ventilation sensor (AC) Respiratory rate)Patency of upper airway Intrathoracic impedance sensor Pulmonarycongestion Transthoracic impedance sensor (DC) Nervous SystemSympathetic nerve activity Muscle sympathetic nerve Activity sensorBrain activity EEG Blood Chemistry CO2 saturation Blood analysis O2saturation Blood alcohol content Adrenalin B-type Natriutetic Peptide(BNP) C-Reactive Protein Drug/Medication/Tobacco use Muscle SystemMuscle atonia EMG sensor Eye movement EOG sensor Patient activityAccelerometer, MV, etc. Limb movements Accelerometer Jaw movements EMGsensor Contextual Environmental Ambient temperature Thermometer HumidityHygrometer Pollution Air quality website Time Clock Barometric pressureBarometer Ambient noise Microphone Ambient light PhotodetectorBody-related Posture Posture sensor Altitude Altimeter Location GPS,proximity sensor Proximity to bed Proximity to bed sensorHistorical/Background Historical sleep time Patient input, previouslydetected sleep onset times Medical history Patient input device AgeRecent exercise Weight Gender Body mass index Neck size Emotional statePsychological history Daytime sleepiness Patient perception of sleepquality Drug, alcohol, nicotine use

Episodes of disordered breathing are associated with acute physiologicaleffects, including, for example, negative intrathoracic pressure,hypoxia, and arousal from sleep. During obstructive apnea, for example,the effort to generate airflow increases. Attempted inspiration in thepresence of an occluded airway result in an abrupt reduction inintrathoracic pressure. The repeated futile inspiratory effortsassociated with obstructive sleep apnea may trigger a series ofsecondary responses, including mechanical, hemodynamic, chemical,neural, and inflammatory responses.

Obstructive sleep apneas are terminated by arousal from sleep severalseconds after the apneic peak, allowing the resumption of airflow.Coincident with arousal from sleep, surges in sympathetic nerveactivity, blood pressure, and heart rate occur. The adverse effects ofobstructive apnea are not confined to sleep. Daytime sympathetic nerveactivity and systemic blood pressure are increased. There may also be asustained reduction in vagal tone, causing reduction in total heart ratevariability during periods of wakefulness.

Central sleep apnea is generally caused by a failure of respiratorycontrol signals from the brain and is a component of Cheyne-Stokesrespiration (CSR), a respiration pattern primarily observed in patientssuffering from chronic heart failure (CHF). Cheyne-Stokes respiration isa form of periodic breathing in which central apneas and hypopneasalternate with periods of hyperventilation causing a waxing-waningpattern of tidal volume. In some patients, obstructive sleep apnea andcentral sleep apnea may coexist. In these patients, there is generally agradual shift from predominantly obstructive apneas at the beginning ofthe night to predominantly central apneas at the end of the night.

When CHF patients lie down, the prone posture may create central fluidaccumulation and pulmonary congestion causing the patient to reflexivelyhyperventilate. Central apnea is usually initiated during sleep by anincrease in ventilation and a reduction of arterial partial pressure ofcarbon dioxide (PaCO₂) that is triggered by spontaneous arousal. WhenPaCO₂ falls below the threshold level required to stimulate breathing,the central drive to the respiratory muscles and airflow cease, andcentral apnea ensues. Apnea persists until PaCO₂ rises above thethreshold required to stimulate ventilation.

Arousals are not required in central sleep apneas for the resumption ofbreathing at the termination of the apneic event. In central apnea, thearousals follow the initiation of breathing and facilitate thedevelopment of oscillations in ventilation by recurrently stimulatinghyperventilation and reducing PaCO₂ below the apneic threshold. Oncetriggered, the pattern of alternating hyperventilation and apnea issustained by the combination of increased respiratory drive, pulmonarycongestion, arousals, and apnea-induced hypoxia causing PaCO₂oscillations above and below the apneic threshold. Shifts in thepatient's state of consciousness, particularly with repeated arousals,may further destabilize breathing.

With the transition from wakefulness to NREM sleep the waking neuraldrive to breathe is lost, and the threshold for a ventilatory responseto CO₂ is increased. Therefore, if the patient's PaCO₂ level duringwakefulness is below this higher sleeping threshold, the transition toNREM sleep may be accompanied by a transient loss of respiratory driveresulting in a central apnea. During the apnea, the PaCO₂ rises until itreaches the new higher threshold level and initiates breathing. If sleepbecomes firmly established, regular breathing resumes. However, if anarousal should occur, the increased PaCO₂ level associated with sleep isnow relatively too high for a state of wakefulness and will stimulatehyperventilation. Thus, although arousals terminate obstructive sleepapneas, arousals trigger the respiratory oscillations associated withcentral apneas, particularly Cheyne-Stokes respiration.

In addition to the acute responses to central sleep apnea discussedabove, central sleep apnea is also associated with a number of secondaryresponses, including, for example, decreased HRV, and blood pressurechanges. Patients with central sleep apnea may have higher urinary andcirculating norepinephrine concentrations and lower PaCO₂ during bothsleep and wakefulness.

FIG. 2 illustrates a block diagram of a disordered breathing therapysystem configured in accordance with embodiments of the invention. It isunderstood that configurations, features, and combinations of featuresdescribed herein can be implemented in a wide range of medical devices,and that such embodiments and features are not limited to the particulardevices described herein.

The system may use patient-internal sensors 210, implanted within thebody of the patient, to detect physiological conditions. For example,the system may determine heart rate, heart rate variability, respirationcycles, tidal volume, and/or other physiological signals using anintracardiac electrocardiogram (EGM) signal detector and transthoracicimpedance sensor that are part of an implanted cardiac rhythm managementsystem such as a cardiac pacemaker or defibrillator.

The system may use patient-external sensors 220 to detect physiologicalor contextual conditions. In one scenario, whether the patient issnoring may be useful in predicting disordered breathing. Snoring may bedetected using an external microphone or an implanted accelerometer. Inanother situation, temperature and humidity may be factors in thepatient's disordered breathing. Signals from temperature and humiditysensors may be used to aid in the prediction of disordered breathing.

Additionally, the system may use information input 230 by the patient toinform the disordered breathing prediction system of one or more patientconditions. In various embodiments, the patient's medical history,self-described medication use, alcohol or tobacco use, day-timesleepiness, or perceptions of sleep quality over the past one or moresleep periods may be useful in connection with the disordered breathingprediction.

Signals from one or more of the patient-internal sensors 210,patient-external sensors 220, and patient input devices 230 may becoupled to a disordered breathing prediction engine 240 for predictionevaluation. In one implementation, the prediction engine 240 may comparethe patient conditions to one or more sets of disordered breathingcriteria and predict disordered breathing based on the comparison. Theprediction engine 240 is coupled to a therapy module 250. If disorderedbreathing is predicted, the therapy module 250 delivers an appropriatetherapy to the patient to mitigate the disordered breathing.

In one example, the patient conditions may be sensed and processed usingimplantable sensors 210, and the prediction analysis and therapydelivery may be performed by a patient-external disordered breathingprediction engine 240 and a patient-external therapy module 250. Some orall of the implantable sensors 210 may have remote communicationcapabilities, such as a wireless proprietary or a wireless Bluetoothcommunications link. In this implementation, the wireless communicationslink couples the implantable sensor or sensors 210 to thepatient-external disordered breathing prediction engine 240. Electricalsignals representing patient conditions are produced by the implantablesensors 210 and transmitted to the patient-external disordered breathingprediction engine 240.

In another example, an implantable therapy device may incorporate adisordered breathing prediction engine 240 and one or morepatient-external sensors 220. Signals representing the patientconditions may be transmitted from the patient-external sensors to theimplanted prediction engine 240 over a wireless communication link.

In a further example, the prediction engine may be a patient-externaldevice coupled wirelessly to the therapy module. Various combinations ofwireless or wired connections between the patient-internal sensors 210,patient-external sensors 220, patient input devices 230, the predictionengine 240, and the therapy module 250 are possible.

The above examples provide a few of the many possible configurationsthat may be used to provide disordered breathing therapy based on theprediction of disordered breathing in accordance with variousembodiments of the invention. It is understood that the components andfunctionality depicted in the figures and described herein can beimplemented in hardware, software, or a combination of hardware andsoftware. It is further understood that the components and functionalitydepicted as separate or discrete blocks/elements in the figures can beimplemented in combination with other components and functionality, andthat the depiction of such components and functionality in individual orintegral form is for purposes of clarity of explanation, and not oflimitation.

The methods and systems for predicting disordered breathing andproviding therapy for disordered breathing as illustrated by theembodiments described herein may be used in cooperation with advancedpatient management systems. Advanced patient management systems allowphysicians to remotely and automatically monitor patient conditions andtest physiological functions, including cardiac and respiratoryfunctions, for example. In one example of advanced patient management,an implantable cardiac rhythm management system, such as cardiacmonitor, pacemaker, defibrillator, or cardiac resynchronization device,may be equipped with various telecommunications and informationtechnologies enabling real-time data collection, diagnosis, andtreatment of the patient. Advanced patient management systems may beenhanced by real-time prediction of disordered breathing and/or longterm collection of disordered breathing prediction data. Systems andmethods involving advanced patient management techniques are describedin U.S. Pat. Nos. 6,336,903, 6,312,378, 6,270,457, and 6,398,728 whichare incorporated herein by reference in their respective entireties.

One subset of the detected patient conditions, such as therepresentative conditions listed in Table 1, may represent conditionsthat predispose the patient to disordered breathing. Predisposingconditions may be statistically associated with an onset of disorderedbreathing during the next few hours following the detection of theconditions leading to the disordered breathing prediction. Anothersubset of conditions may represent precursor conditions used to predictan imminent onset of disordered breathing that may occur within a timewindow measured in terms of a few minutes or seconds. Detection ofpatient conditions associated with disordered breathing and predictionof disordered breathing based on predisposing or precursor conditions isperformed on real-time basis.

A subset of patient conditions may be used to verify or otherwise informthe disordered breathing prediction. In one example, informationregarding sleep onset or sleep state, e.g., REM or non-REM sleep, may beemployed in prediction of sleep disordered breathing. A subset of theconditions listed in Table 1 may be used to detect whether the patientis asleep and to track the various stages of sleep. Another subset ofthe conditions may be employed to detect and classify disorderedbreathing episodes. Table 2 below provides further examples of how someconditions listed in Table 1 may be used in disordered breathingprediction.

TABLE 2 Condition Examples of how condition is used in disorderedbreathing prediction Heart rate Decrease in heart rate may indicatedisordered breathing episode. Decrease in heart rate may indicate thepatient is asleep. Heart rate variability May be used to determine sleepstate and reduction in heart rate variability is a chronic factorassociated with disordered breathing. Ventricular filling pressure Maybe used to identify/predict pulmonary congestion associated withrespiratory disturbance. Blood pressure Swings in on-line blood pressuremeasures are associated with apnea. Snoring Snoring is associated with ahigher incidence of obstructive sleep apnea and may be used to detectdisordered breathing. Respiration signals/respiration Respirationpatterns may be used to detect disordered breathing patterns episodes.Respiration patterns may be used to determine the type of disorderedbreathing. Respiration patterns may be used to detect that the patientis asleep. Hyperventilation may be used to predict disordered breathing.Previous episodes of disordered breathing may be used to predict furtherepisodes. One form of disordered breathing may be used to predictanother form of disordered breathing Patency of upper airway Patency ofupper airway is related to obstructive sleep apnea and may be used todetect episodes of obstructive sleep apnea. Pulmonary congestionPulmonary congestion is associated with respiratory disturbances.Sympathetic nerve activity End of apnea associated with a spike in SNACO2 saturation Low CO2 levels initiate central apnea. O2 saturation O2desaturation occurs during severe apnea/hypopnea episodes. Blood alcoholcontent Alcohol tends to increase incidence of snoring & obstructiveapnea. Adrenalin End of apnea associated with a spike in bloodadrenaline. Brain Natriuretic Peptide A marker of heart failure status,which is associated with Cheyne- (BNP) Stokes Respiration C-ReactiveProtein A measure of inflammation that may be related to apnea.Drug/Medication/Tobacco use These substances may affect the likelihoodof both central & obstructive apnea. Muscle atonia Muscle atonia may beused to detect REM and non-REM sleep. Eye movement Eye movement may beused to detect REM and non-REM sleep. Temperature Ambient temperaturemay be a condition predisposing the patient to episodes of disorderedbreathing. Humidity Humidity may be a condition predisposing the patientto episodes of disordered breathing. Pollution Pollution may be acondition predisposing the patient to episodes of disordered breathing.Posture Posture may be used to determine if the patient is asleep andmay predispose the patient to disordered breathing. Posture may be acondition predisposing the patient to episodes of disordered breathing.Activity Patient activity may be used in relation to sleep detection.Sleep stage NREM sleep may be associated with a higher probability of DBLocation Patient location may used to determine if the patient is in bedas a part of sleep detection.

FIG. 3 conceptually illustrates how patient conditions such as thoselisted in Table 1 and/or 2 may be used in predicting disorderedbreathing 310 according to embodiments of the invention. In oneembodiment, the system tracks one or more of the conditions listed inTable 1, Table 2, or both, to predict disordered breathing. For example,over the course of a period of time, e.g., at least a 16 hour windowpreceding and including the patient's historical sleep time, the systemmay track one or more conditions to determine the presence and/or levelof each particular condition.

In one implementation, the system tracks conditions that have beendetermined to predispose 320 the patient to an attack of disorderedbreathing. Predisposing conditions represent patient conditionsstatistically associated with an onset of disordered breathing. Thepresence of one or more predisposing conditions may indicate thatdisordered breathing is likely to occur within the next time period,such as an eight hour period following the disordered breathingprediction, or during the current sleep period. For example, thepredisposing conditions may include the air pollution index of thepatient's environment downloaded from an air quality website, recenttobacco use reported by the patient, the degree of the patient'spulmonary congestion detected by an implanted transthoracic impedancesensor, as well as other patent internally or externally detectedpredisposing conditions.

Additionally, or alternatively, the system may use previous episodes ofdisordered breathing to determine that the patient is predisposed tofurther episodes of disordered breathing within particular time period,such as during a sleep period. For example, previous episodes ofdisordered breathing during a first interval within the sleep period maybe an indication that additional episodes are likely to occur in asecond and subsequent interval within the same sleep period. In oneexample, the occurrence of a first type of disordered breathing may beused to predict a second type of disordered breathing. In anotherexample, the periodicity of disordered breathing may be used to predictfuture episodes of disordered breathing.

The disordered breathing prediction engine may use the type, duration,frequency, and/or severity of the previous disordered breathing episodesto inform the disordered breathing prediction analysis. Quantificationof the severity, frequency, and duration of disordered breathing may beaccomplished using any of a number of disturbed breathing measures,including, for example, percent time in disordered breathing and theapnea/hypopnea index.

A further example of a condition predisposing a patient to hypopnea orapnea is body posture. A supine posture is more likely to result inobstruction of the upper airway and can be used to predict episodes ofobstructive hypopnea and apnea. Posture and/or torso orientation sensingmay be accomplished, for example, using an implantable multiaxisaccelerometer.

As previously discussed, sleep disordered breathing is a prevalent formof disordered breathing. Thus, a patient may be more likely toexperience episodes of disordered breathing when the patient is in bedsleeping. Thus, proximity to bed may be employed as a predisposingcondition to disordered breathing. The disordered breathing therapysystem may use a bed proximity sensor to detect that the patient is inbed. Bed proximity may be detected by placing a beacon transmitter onthe patient's bed. Receiver circuitry on or in the patient, for example,incorporated in the patient's pacemaker, receives the beacon signal anddetermines that the patient is in bed.

Conditions that predispose the patient to disordered breathing 320 areconditions that indicate the likelihood that one or more episodes ofdisordered breathing will occur during the next time period, such asover the course of the night or other sleep period. Based onpredisposing conditions 320, an onset of disordered breathing may bepredicted 312 to occur within a time window that may include severalhours, for example, eight hours.

A second set of conditions, denoted herein as precursor conditions 330,may be used to predict 314 an impending onset of disordered breathing.Precursor conditions 330 indicate that an episode of disorderedbreathing is imminent and will occur within a time window that may bemeasured in terms of minutes or seconds, for example. In oneimplementation, precursor conditions 330 may be used to predict that anepisode of disordered breathing will occur within the next 300 seconds,for example.

In one embodiment, precursor conditions 330 indicative of an impendingonset of disordered breathing may include, for example, pre-apnea orpre-hypopnea conditions. In one implementation, changes in blood gasconcentration, such as CO₂, may be causal to central apnea. Therefore, aprecursor condition of pre-apnea in a particular patient may be detectedwhen the patient's CO₂ level, as measured, for example, by apatient-external CO₂ sensor, falls below a selected level, indicating animpending onset of an apnea episode.

In another embodiment, a patient's heart rate variability may besignificantly altered before, during, and after episodes of apnea. Heartrate variability may be used, for example, as a precursor condition topredict an impending episode of disordered breathing.

In yet another embodiment, a pre-apnea or pre-hypopnea condition may bedetected by analyzing the patient's respiration patterns. Respirationcycles just prior to disordered breathing event, e.g., an apnea orhypopnea event, may exhibit a characteristic pattern. For example, anapnea event for many patients is preceded by a period ofhyperventilation with a number of rapid, deep breaths. The pattern ofhyperventilation may be detected by analyzing patient's transthoracicimpedance signal to determine respiration rate and tidal volume.

Cheyne-Stokes respiration and some apnea/hypopnea episodes may exhibit acrescendo—decrescendo respiration pattern. The crescendo—decrescendorespiration pattern produces hyperventilation during the crescendo stageand hypoventilation during the decrescendo phase. Hyperventilation,secondary to pulmonary congestion, drives arterial partial pressure ofcarbon dioxide down. A decrease in arterial partial pressure of carbondioxide below an apnea level may be a causal mechanism for centralapnea. According to one embodiment of the invention, detection of animpending onset of disordered breathing may be implemented by detectinga series of increasing tidal volumes followed by a series of decreasingtidal volumes.

For some patients, disordered breathing occurs at regular intervals,allowing the periodicity of the disordered breathing episodes to be usedas a precursor condition. If disordered breathing episodes of thepatient occur at regular intervals, the next episode of disorderedbreathing may be predicted based on the time elapsed since the lastepisode was detected.

In addition, the occurrence of one form of disordered breathing may beused to predict another form of disordered breathing. For example, apatient may characteristically experience one or more episodes ofobstructive sleep apnea during the first part of the night followed bycentral sleep apnea episodes during the later part of the night. Inanother example, one or more episodes of hypopnea may be used to predictfuture apnea episodes.

Snoring is an additional example of a pre-apnea or pre-hypopneacondition. In many, patient snoring, or more generally any abnormalairflow in the upper airway, which may be detectable via acoustic means,precedes more significant sleep disordered breathing conditions such ashypopnea or apnea. Precursor conditions 330 may be analyzedindividually, or in combination with one or more predisposing conditions320, to predict the impending onset of a disordered breathing episode.

The conditions and associated prediction criteria used for disorderedbreathing prediction may be highly patient specific. Conditions that arereliably predictors of disordered breathing in one patient may not beeffective for another patient. Therefore, conditions used to predictdisordered breathing and the respective prediction criteria arepreferably based on patient-specific data.

A subset of patient conditions may be used to verify or confirm aprediction of disordered breathing. For example, before or after aprediction of disordered breathing is made, one or more verificationconditions 340 may be checked to confirm the prediction. Theverification conditions, as well as the physiological and contextualconditions used to predict disordered breathing, may be highly patientspecific.

In one example embodiment, a characteristic pattern of respiration is areliable predictor of disordered breathing in a particular patient onlywhen the patient is supine. If the prediction is made while the patientnot supine, normal variations in respiration cycles in this particularpatient may lead to an erroneous prediction of disordered breathing.Thus, before disordered breathing is predicted, a posture sensor signalis checked to verify that the patient is supine. If the patient issupine and the patient's respiration cycles are consistent with criteriaindicating that disordered breathing is likely, the disordered breathingprediction is made.

In another example, the patient is known to suffer from episodes ofapnea during sleep. The patient's sleep apnea may be predicted using anumber of contextual and physiological conditions. The prediction ofsleep apnea may be made after assessing that the patient's posture andlocation are consistent with sleep. Before a prediction of sleep apneais made, the system confirms that the patient is lying down in bed bychecking the signal from an implantable posture sensor and a bedproximity sensor.

Alternatively, or additionally, the system may detect that the patientis sleeping by examining the patient's respiration and/or activity priorto making a prediction regarding sleep disordered breathing. A method ofsleep detection is described in commonly owned U.S. patent applicationSer. No. 10/309,771, filed Dec. 4, 2002, which is incorporated herein byreference in its entirety.

The operation of a disordered breathing prediction engine 400, accordingvarious to embodiments, is conceptually illustrated in the block diagramof FIG. 4. Periodically, one or more patient conditions are detected andcompared to a library 410 of prediction criteria. The predictioncriteria library 410 may incorporate one or more sets of predictioncriteria 411, 412, 413, 414. Each of these sets of criteria may becompared to the detected patient conditions. If the criteria of aprediction criteria set 411, 412, 413, 414 are substantially consistentwith the patient conditions, a preliminary disordered breathingprediction may be made.

In various embodiments, the prediction criteria sets 411, 412, 413, 414represent one or more condition thresholds associated with an onset ofdisordered breathing. In one example embodiment, the level of one ormore detected conditions may be compared to the prediction criteria sets411, 412 413, 414. If the levels of the one or more conditions aresubstantially consistent with the thresholds specified in a predictioncriteria set 411, 412, 413, 414, a preliminary prediction of disorderedbreathing may be made.

The examples that follow are described in terms of a condition beingconsistent with a prediction criteria when the condition exceeds aprediction criteria threshold. However, it will be understood thatdifferent threshold requirements may be defined for differentconditions. For example, one condition may be defined to be consistentwith a prediction criterion when the condition exceeds a predictioncriterion threshold. Another condition may be defined to be consistentwith a prediction criterion threshold when the condition falls below thethreshold. In yet another example, a condition may be defined to beconsistent with the prediction criterion when the condition falls withina specified range of values. Patient conditions may be compared toprediction criteria based on the timing, rate of change, or maximum orminimum value of the condition, for example.

In the example provided in FIG. 4, the prediction criteria N 414involves two contextual conditions, C1 and C2, and two physiologicalconditions, P1 and P2. In this particular example, if conditions C1, C2,P1, and P2 exceed levels Level1, Level2, Level3, and Level4,respectively, the patient may be likely to experience disorderedbreathing during the night. Therefore, when conditions C1, C2, and P1,P2 reach the levels specified in criteria N 414, preliminary predictionof disordered breathing is made.

In another embodiment of the invention, the relationships between thedetected conditions are analyzed to predict disordered breathing. Inthis embodiment, the disordered breathing prediction may be based on theexistence and relative values associated with two or more patientconditions. For example, if condition A is present at a level of x, thencondition B must also be present at a level of f(x) before a disorderedbreathing prediction is made.

In yet another embodiment of the invention, the estimated probability,P(C_(n)), that disordered breathing will occur if a particular conditionlevel is detected may be expressed as a function of the ratio of thenumber of times disordered breathing occurred within a selected timeinterval following the detection of the particular condition level tothe total number of observed occurrences of the condition level. Theprobability that disordered breathing will occur, P(C_(n)), is comparedto a threshold probability level to make the disordered breathingprediction. Other methods of calculating the estimated probability arealso possible.

The prediction of disordered breathing may be based on the convergenceor divergence of a number of conditions occurring within the same timeperiod. In this situation, a composite probability score may be computedas a combination of the individual probabilities. In one embodiment, theprobabilities are combined by adding the condition probabilities aftermultiplying each of the condition probabilities by a weighting factor.For example, if the disordered breathing prediction is based on foursubstantially simultaneous conditions, C₁, C₂, C₃, and C₄, the totalprobability score PS_(T) may be calculated as:

PS _(T) =A×P(C ₁)+B×P(C ₂)+C×P(C ₃)+D×P(C ₄),  [1]

where A, B, C, and D are scalar weighting factors that may be used toestimate the relative importance of each of the conditions C₁, C₂, C₃,and C₄. If the probability score exceeds a selected prediction criteriathreshold, then disordered breathing is predicted.

Although the above process describes combining the estimatedprobabilities for each condition by adding each of the estimatedprobabilities, other methods are also possible. For example, a detectedpatient condition may operate against a prediction of disorderedbreathing. In this situation, the estimated probability, P_(n)(C_(n)),that disordered breathing will not occur if a particular condition levelis detected may be expressed as a function of the ratio of the number oftimes disordered breathing did not occur within a selected time intervalfollowing the detection of the particular condition level to the totalnumber of observed occurrences of the condition level. This value may besubtracted from the total to determine the probability score. Non-linearmethods of combining the estimated probabilities to arrive at acomposite probability are also possible.

If the conditions affecting the patient are consistent with a predictionof disordered breathing, the prediction may be verified by comparing oneor more verification conditions to verification criteria. If theverification conditions are consistent with the verification criteria, aprediction of disordered breathing is made.

In the embodiments described above, predictions of disordered breathingare based upon comparisons of one or more patient conditions to sets ofprediction criteria. The initial data from which the initial predictioncriteria sets are formed may be derived from past observations takenfrom population data, or from data collected from a particular patient.The initial prediction criteria sets may then be modified as additionaldata are collected from the patient.

In one embodiment, an estimated accuracy for the prediction criteria isupdated for every prediction event. The estimated positive predictivevalue (PPV) for a prediction criteria set N may be expressed as:

$\begin{matrix}{{PPV}_{N} = \frac{TP}{{TP} + {FP}}} & \lbrack 2\rbrack\end{matrix}$

where TP (true positive) is the number of times the prediction criteriaset successfully predicted disordered breathing, and FP (false positive)is the number of times the prediction criteria erroneously predicteddisordered breathing.

If the estimated accuracy of prediction criteria set N, PPV_(N), fallsbelow a predetermined level, for example, 0.7, the prediction criteriaset N may be modified. In one embodiment, a possible prediction criteriaset is formed, for example, by modifying the threshold level of one ormore of the conditions represented by the original prediction criteriaset N. In one embodiment, each threshold in the original predictioncriteria set N is modified by an incremental value, to make theprediction criteria set more accurate.

In another embodiment, conditions represented in the original predictioncriteria set N are compared to the conditions that are present justprior to a disordered breathing occurrence to determine how themodification for the possible prediction criteria set should beimplemented. For example, if the level of a particular condition justprior to the occurrence shows a relatively large variation just prior tothe disordered breathing episode, but the levels of other conditionsremain constant, then only the changing level may be modified in thepossible prediction criteria set.

Each time the possible prediction criteria set is satisfied, noprediction of disordered breathing is made, however, the accuracy of thepossible prediction criteria set is updated, for example, using anequation similar in form to Equation 2. If the accuracy of the possibleprediction criteria set reaches a selected level, for example, 0.7, andthe accuracy original prediction criteria set N remains below 0.7, thepossible prediction criteria set may replace the original predictioncriteria set N in the prediction criteria library.

According to various embodiments, new prediction criteria sets may beadded to the prediction criteria library. In accordance with theseembodiments, if a disordered breathing episode occurs withoutprediction, the levels of the detected patient conditions prior to thedisordered breathing episode are saved as a possible prediction criteriaset. Each time the possible prediction criteria set is satisfied, noprediction of disordered breathing is made, however, the accuracy of thepossible prediction criteria set is updated, for example, using anequation similar in form to Equation 2. If the accuracy of the possibleprediction criteria set reaches a selected level, for example, 0.7, thepossible prediction criteria set may be added to the prediction criterialibrary.

The system may also be adjusted to provide increasingly sensitivedisordered breathing prediction criteria sets, according to variousembodiments. The estimated sensitivity for a prediction criteria set Nmay be expressed as:

$\begin{matrix}{{Sensitivity}_{N} = \frac{TP}{{TP} + {FN}}} & \lbrack 3\rbrack\end{matrix}$

where TP (true positive) is the number of times the prediction criteriasuccessfully predicted disordered breathing, and FN (false negative) isthe number of times the prediction criteria erroneously predicted thatdisordered breathing would not occur.

In one embodiment, if the prediction criteria accuracy for theprediction criteria set N becomes larger than a selected number, forexample, 0.9, then the threshold levels of one or more of the conditionsrepresented in the prediction criteria set N may be adjusted to provideenhanced sensitivity.

In one example, the threshold level of each condition represented in theprediction criteria set N is modified by an incremental value, thusmaking the prediction criteria set N more sensitive. In anotherembodiment, conditions represented in the prediction criteria set N arecompared to the conditions that are present just prior to a disorderedbreathing occurrence to determine how the modification of the predictioncriteria set N should be implemented. In yet another embodiment, acondition threshold level that is modified is based upon the relativeimportance of the condition in the overall prediction criteria. Inanother example, if the level of a particular condition is changing justprior to the occurrence of the disordered breathing episode, but thelevels of other conditions remain constant, only the changing conditionmay be modified.

Following adjustment by any of the processes described above, theadjusted prediction criteria set may be designated as a possibleprediction criteria set. Each time the possible prediction criteria setis satisfied, no prediction of disordered breathing is made, however,the accuracy of the possible prediction criteria set is updated, forexample, using Equation 2 or 3. If the accuracy of a possible predictioncriteria set reaches a selected level, for example, 0.7, the possibleprediction criteria set may be added to the prediction criteria library.

The system may also be adjusted to provide improved specificity or anegative predictive value (NPV) of disordered breathing predictioncriteria in a manner similar to the adaptive method describedpreviously. Calculation of specificity and NPV for a prediction criteriaN may be accomplished using equations 4 and 5 below.

$\begin{matrix}{{Specificity}_{N} = \frac{TN}{{TN} + {FP}}} & \lbrack 4\rbrack \\{{NPV}_{N} = \frac{TN}{{TN} + {FN}}} & \lbrack 5\rbrack\end{matrix}$

where TN (true negative) is the number of times the prediction criteriasuccessfully predicted the absence of disordered breathing, FP (falsepositive) is the number of times the prediction criteria erroneouslypredicted disordered breathing and FN (false negative) is the number oftimes the prediction criteria erroneously predicted the absence ofdisordered breathing.

The flowchart of FIG. 5 illustrates a method for establishing andupdating the prediction criteria library according to embodiments of theinvention. Previous observations of disordered breathing may beassimilated from population data 502 or from past observation of thespecific patient 504. One or more prediction criteria sets aredetermined and organized in a prediction criteria library 506.

Conditions associated with disordered breathing are periodicallydetected 508 and compared to the prediction criteria sets in theprediction criteria library. If the conditions are consistent 510 withany of the prediction criteria sets in the library, then disorderedbreathing is predicted 515. Within a selected time window following thedisordered breathing prediction, the system determines if disorderedbreathing occurs 520.

One illustrative approach to detecting disordered breathing involvesmonitoring a respiratory waveform output, for example, using atransthoracic impedance sensor. When the tidal volume (TV) of thepatient's respiration, as indicated by the transthoracic impedancesignal, falls below a hypopnea threshold, then a hypopnea event isdeclared. For example, a hypopnea event may be declared if the patient'stidal volume fall below about 50% of the recent average tidal volume orother baseline tidal volume. When the patient's tidal volume fallsfurther to an apnea threshold, e.g., about 10% of the recent averagetidal volume, an apnea event is declared.

Other approaches to detecting disordered breathing are described incommonly owned U.S. patent application Ser. No. 10/309,770, filed Dec.4, 2002, which is incorporated herein by reference in its entirety.

If disordered breathing occurs 520, the prediction criteria accuracy ofthe prediction criteria set used for the disordered breathing predictionis updated 525. If the updated prediction criteria accuracy is greater530 than a selected number, then a possible prediction criteria set isformed 535. The possible prediction criteria set may be formed, forexample, by substituting more sensitive condition levels when comparedto the original prediction criteria set.

If disordered breathing is not detected 520 following the prediction,then the prediction criteria set accuracy is updated 540. If theprediction criteria set accuracy decreases 545 below a selected number,then a possible prediction criteria set 550 is formed. The possibleprediction criteria set may be formed, for example, by substituting morestringent condition levels to produce a more accurate prediction.

If the detected patient conditions are not consistent 510 with any ofthe prediction criteria sets in the prediction criteria library,disordered breathing is not predicted. Within a time window followingthe disordered breathing prediction, the system determines if disorderedbreathing occurs 555. If disordered breathing occurs 555, then thesystem checks to see if the patient conditions are consistent 560 withany of the possible prediction criteria sets. If the patient conditionsare not consistent 560 with any of the possible prediction criteriasets, a possible prediction criteria set is formed 565.

If the patient conditions are consistent 560 with a possible criteriaset, the possible prediction criteria set accuracy is updated 570. Ifthe possible prediction criteria accuracy increases beyond a selectednumber 575, the possible prediction criteria set is added 580 to theprediction criteria library.

The block diagram of FIG. 6 illustrates a disordered breathing therapysystem configured in accordance with embodiments of the invention.According to one embodiment, a disordered breathing prediction engine642 is incorporated within a cardiac rhythm management system 600. Thecardiac rhythm management system may include, for example, a cardiactherapy module 620 including a pacemaker 622 and an arrhythmiadetector/therapy unit 624. The cardiac therapy module 620 is coupled toa lead system having electrodes 631 implanted to electrically couple theheart 630 to the cardiac rhythm management system 600.

The cardiac rhythm management system 600 includes circuitry 650 fordetecting signals from patient-internal sensors such as the implantedcardiac electrodes 631, and other patient-internal sensors 680, such asthe patient-internal sensors listed in Table 1. The patient-internalsensors 680 may be coupled to the implanted signal detection circuitry650 through conducting leads as shown, or through a wireless connection,for example.

The cardiac rhythm management system 600 may also include circuitry 660for detecting signals from patient-external sensors 690 located outsidethe patient's body and from patient-reported input. The patient-externalsensors 690 may be coupled to the detection circuitry 660 through awireless link. Signals representing patient-reported data may be inputthrough a programmer unit 670 that is wirelessly coupled to a telemetrycircuit 675 within the cardiac rhythm management system 600.

The cardiac therapy module 620 receives cardiac signals from theimplanted cardiac electrodes 631 and analyzes the cardiac signals todetermine an appropriate therapy. The cardiac therapy may include pacingtherapy controlled by the pacemaker 622 to treat cardiac rhythms thatare too slow. The pacemaker 622 controls the delivery of periodic lowenergy pacing pulses to one or more of the heart chambers throughcardiac electrodes 631 to ensure that the periodic contractions of theheart are maintained at a hemodynamically sufficient rate.

The cardiac therapy may also include therapy to terminatetachyarrhythmia, wherein the heart rate is too fast. The arrhythmiadetector/therapy unit 624 detects and treats episodes oftachyarrhythmia, including tachycardia and/or fibrillation. Thearrhythmia detector/therapy unit 624 recognizes cardiac signalsindicative of tachyarrhythmia and delivers high energy stimulations tothe heart 630 through the implanted electrodes 631 to terminate thearrhythmia.

A disordered breathing module 640 incorporated within the cardiac rhythmmanagement system 600 includes circuitry for disordered breathingdetection 644, as well as the disordered breathing prediction engine642. The implanted signal detection circuitry 650 andpatient-reported/patient-external sensor detection circuitry 660 arecoupled to the disordered breathing module 640. The implanted signaldetection circuitry 650 and patient-reported/patient-external sensordetection circuitry 660 provide signals associated with various patientconditions used for disordered breathing detection and prediction.

A prediction of disordered breathing by the disordered breathingprediction engine 642 may be used to trigger cardiac pacing therapydelivered by the cardiac therapy module 620 to mitigate disorderedbreathing. In one example, the cardiac therapy module 620 delivers anappropriate electrical stimulation therapy to the patient's heart 630through electrodes 631 coupled to the patient's heart. In one exampletherapy regime, electrical stimulation therapy to mitigate disorderedbreathing may include pacing at a rate exceeding an intrinsic rate or inexcess of the patient's normal sleep rate. The pacing may involve any orall of the heart chambers, for example, right and left atria and rightand left ventricles. The pacing may also involve bi-atrial,bi-ventricular, or multi-site pacing. In one example, the pacing pulsesmay be delivered to left and right atria simultaneously, or according toother timing sequences. In another example, the simultaneous orotherwise timed pacing pulses may be delivered to the left and rightventricles of the heart.

Further, adapting a cardiac electrical therapy to mitigate disorderedbreathing may involve adapting a therapy involving non-excitatoryelectrical stimulation of one or more heart chambers, e.g., the leftand/or right ventricles, or other cardiac sites. Non-excitatoryelectrical stimulation may be delivered during absolute refractoryperiods of the cardiac tissue, for example, to improve cardiaccontractility. The non-excitatory stimulation therapy may be used aloneor in combination with cardiac pacing to provide a comprehensive therapyregimen for patients with CHF and disordered breathing such asCheyne-Stokes respiration.

FIGS. 7 through 9 illustrate systems that may be used to implementmethods of disordered breathing prediction according to embodiments ofthe invention. FIG. 7 illustrates a system for delivering disorderedbreathing therapy utilizing the cardiac rhythm management systemincorporating a disordered breathing prediction engine 710 as discussedin connection with FIG. 6. In addition to the previously describedimplanted cardiac electrodes, the cardiac rhythm management system 710also includes an accelerometer mounted within the housing of the cardiacrhythm management system 710 for sensing patient activity.

In the embodiment of FIG. 7, the cardiac rhythm management system 710further includes a receiver for a proximity to bed signal that isgenerated by a proximity to bed beacon 720 located on or near thepatient's bed 730. If the proximity to bed receiver detects a signal ofsufficient strength from the proximity to bed beacon 720, then thereceiver signals that the patient is in bed.

The cardiac rhythm management system 710 includes a transthoracicimpedance sensor used to determine patient conditions includingrespiration rate, respiration rate variability, tidal volume, and minuteventilation, for example. In this example, the disordered breathingprediction engine located within the cardiac rhythm management system710 predicts disordered breathing based primarily on the patient's heartrate and tidal volume. Two additional signals, the patient's activitylevel and proximity to bed, are used to verify the prediction ofdisordered breathing. After a prediction of disordered breathing, thecardiac rhythm management system delivers an appropriate cardiac therapyto mitigate the disordered breathing.

Representative graphs of the patient's tidal volume 810, heart rate 820,and activity level 830 during disordered breathing prediction areillustrated in FIGS. 8A-8C. In this example, the patient's tidal volume810 exhibits a characteristic decrease 812 just before the onset 840 ofan episode of disordered breathing 850. Accordingly, a first conditionthreshold for disordered breathing prediction is established as apercentage drop in tidal volume. Additionally, the patient's heart rate820 exhibits a decrease 822 that occurs substantially simultaneouslywith the decrease in tidal volume 812. A second condition threshold fordisordered breathing detection is established as a percentage drop inheart rate. If disordered breathing is predicted, therapy may bedelivered to the patient, for example, in accordance with variouscardiac pacing regimens described above.

Therapy delivered to mitigate disordered breathing may be adjusted basedon an assessment of the therapy. Therapy assessment may include, forexample, assessment of the efficacy of therapy or assessment of theimpact of the therapy on the patient. According to various embodiments,therapy efficacy may be analyzed and the therapy regimen may be adaptedto provide more effective therapy. For example, if a delivered therapydoes not prevent or otherwise mitigate the patient's disorderedbreathing, the therapy may be modified to include a more aggressivetherapy regimen, e.g., cardiac pacing at a higher rate.

According to embodiments of the invention, the therapy may be adapted toreduce the impact of the therapy on the patient, e.g., to minimallyimpact the patient. In adapting a reduced impact therapy, the system maytake into account various conditions for evaluating the impact of thetherapy on the patient. For example, conditions such as patient comfort,as indicated by patient feedback, stress on physiological systemsinvolved in the disordered breathing therapy, interaction with cardiacpacing algorithms, e.g., bradycardia pacing, cardiac resynchronizationpacing an/or anti-tachycardia pacing, as determined by interactiveeffects of the disordered breathing therapy with cardiac pacing, and/orsleep quality, as measured by one or more sleep quality indices, may betaken into account to adapt a therapy that reduces an impact of thetherapy on the patient.

In addition, impact to the patient may involve reduction of the usefulservice life of an implantable therapeutic device used to deliverdisordered breathing therapy and/or pacing therapy for cardiacdysfunction. For example, a level of disordered breathing therapy may beunacceptably high if the energy requirements of the therapy result in anexcessively reduced device service life. In this situation, early deviceremoval and replacement produces a negative impact to the patient.Therefore, cardiac electrical therapy to mitigate disordered breathingmay be adapted based on a projected reduction in device service life.

In one example implementation, pacing above a normal sleep rate maymitigate disordered breathing, but may coincidentally interrupt thepatient's sleep, causing sleep fragmentation and other undesirableeffects. The therapy may be adapted to reduce an impact of the therapyon the patient. For example, if the delivered therapy causes anundesirable number of arousals from sleep, the therapy may be adjustedto a less aggressive therapy regimen, e.g., cardiac pacing at a lowerrate.

In another example, the therapy delivered to mitigate disorderedbreathing may be adapted to reduce interactions between the disorderedbreathing therapy and other therapies delivered to the patient. Forexample, some patients may receive one cardiac electrical stimulationtherapy to treat disordered breathing and other cardiac stimulationtherapy to treat cardiac disorders such as bradycardia or CHF.Interactions may occur between cardiac electrical therapy to mitigatedisordered breathing and the patient's cardiac pacing regimen, e.g.,pacing for bradycardia or cardiac resynchronization. Such interactionsmay be factored into the assessment of the impact disordered breathingtherapy on the overall therapy delivered to the patient.

In some cases cardiac electrical therapy to mitigate disorderedbreathing may enhance cardiac pacing therapy directed to alleviate acardiac dysfunction, e.g., bradycardia or CHF. For example,non-excitatory electrical stimulation of the left ventricle during anabsolute refractory period may be beneficial to treat CHF and disorderedbreathing. In other cases, cardiac electrical therapy for disorderedbreathing may work at cross purposes with the patient's cardiac pacingregimen. For example, pacing therapy delivered to treat disorderedbreathing may increase the percentage of heart beats initiated by atrialpacing while cardiac resynchronization therapy may be optimal whenintrinsic atrial events are allowed to initiate a heart beat. Evaluatingthe impact of disordered breathing therapy on the patient preferablytakes into consideration the impact of disordered breathing therapy onthe overall therapeutic goals for the patient, including cardiac pacinggoals and disordered breathing goals.

FIG. 9 is a flowchart illustrating a method of providing therapy fordisordered breathing according to embodiments of the invention.According to this method, one or more patient conditions are detected910 and a first group of the detected conditions are used to predict 920disordered breathing. A therapy is adapted to mitigate 930 or preventthe disordered breathing based on a second set of the patientconditions. The adapted therapy is delivered 940 to the patient.

A representative set of the first and second groups of patientconditions that may be used for disordered breathing prediction andtherapy assessment, respectively, is provided in Table 1. As previouslydiscussed, a first group or subset of conditions is used in connectionwith disordered breathing prediction. a second group of conditions,possibly overlapping the first group, is used for therapy assessment.Several aspects of therapy may be assessed. In one embodiment, therapyis assessed based on therapy effectiveness. In another embodiment,therapy is assessed based on minimal impact to the patient. In yet afurther embodiment, therapy is assessed based on a combination oftherapy effectiveness and minimal impact to the patient.

As previously discussed, therapy assessment may be implemented bydetecting and analyzing one or more patient conditions. Conditions usedto assess therapy effectiveness may be different from, or the same as,conditions used to assess the impact of the therapy on the patient.Table 3 provides a representative set of conditions that may be used fortherapy assessment.

TABLE 3 Condition Therapy Impact Therapy Efficacy Arousal-Based SleepMay be used to assess therapy Fragmentation Measures impact duringsleep. Restful sleep (Patient reported) May be used to assess therapyimpact during sleep. Discomfort (Patient reported) May be used to assesstherapy impact. Pacing algorithm interaction May be used to assesstherapy impact. Remaining useful life of therapy May be used to assesstherapy device impact. Pacing algorithm interaction May be used toassess therapy impact during sleep. Disturbed Breathing-Based May beused to analyze/assess efficacy of Measures therapy to mitigatedisordered breathing episodes. Respiration quality (Patient May be usedto analyze/assess efficacy of reported) therapy to mitigate disorderedbreathing episodes. Heart rate variability (HRV) Disordered breathingcauses heart rate variability to decrease. Therapy may be modified basedon changes in HRV Blood pressure Disordered breathing causes bloodpressure increase Sympathetic nerve activity Changes in sympatheticnerve activity are (SNA) caused by disordered breathing. Therapy may beadjusted based on the level of SNA Blood chemistry A number ofdisordered breathing related changes may occur in a patient's bloodchemistry, including, e.g., higher norepinephrine levels, and lowerPaCO₂

It is understood that the patient conditions that may be used inconnection with prediction of disordered breathing and/or therapyassessment are not limited to the representative sets listed in Tables1-3. Further, although illustrative sensing methods for detecting thepatient conditions are provided, it is understood that the patientconditions may be sensed and detected using a wide variety oftechnologies. The embodiments and features described in the instantdisclosure are not limited to the particular patient conditions or theparticular sensing technologies described herein.

In one example, conditions related to sleep quality, e.g., sleepfragmentation and other arousal-based measures, patient-reported restfulsleep, and discomfort during therapy, may be used to assess the impactof the therapy on the patient. For example, if a patient receivingeffective disordered breathing therapy has low sleep fragmentation,reports restful sleep, and reports no discomfort, the adverse effects ofthe therapy on the patient may be relatively low. If sleep fragmentationis relatively high, or if the patient reports discomfort or feelingtired after sleeping, these conditions may indicate that therapy iscausing sleep disturbance and/or other undesirable effects. Variousmethods and systems for collecting sleep quality data and assessingsleep quality are described in a commonly owned U.S. Publication No.2005/0042589, filed concurrently with this application which is herebyincorporated herein by reference.

Sleep fragmentation and sleep disruptions may also occur if disorderedbreathing therapy is ineffective and disordered breathing occurs duringsleep. Therefore, a therapy impact assessment based on detected sleepquality and/or patient-reported restful sleep preferably takes intoaccount an assessment of therapy effectiveness.

Some patients may receive cardiac electrical stimulation therapy forboth disordered breathing as well as cardiac disorders such asbradycardia and/or CHF. Interactions may occur between cardiacelectrical therapy to mitigate disordered breathing and the patient'scardiac pacing regimen, e.g., pacing for bradycardia or cardiacresynchronization. Such interactions may be factored into the assessmentof the impact of disordered breathing therapy on the overall therapydelivered to the patient.

Interactions between cardiac therapy and disordered breathing therapymay occur, and detection of the interactions may be used to adjusttherapy. In some cases, cardiac electrical therapy to mitigatedisordered breathing may enhance cardiac pacing therapy directed toalleviate a cardiac dysfunction, such as bradycardia or CHF. Forexample, non-excitatory electrical stimulation of the left ventricleduring an absolute refractory period may be beneficial to treat both CHFand disordered breathing.

In other examples, cardiac electrical therapy for disordered breathingmay work at cross purposes with the patient's cardiac pacing regimen. Apacing therapy delivered for treatment of disordered breathing mayincrease the percentage of heart beats initiated by atrial pacing.However, a concurrent cardiac resynchronization therapy may be optimalwhen intrinsic atrial events are allowed to initiate a heart beat. Inthis situation, the disordered breathing therapy, the cardiacresynchronization therapy, or both therapies, may be adjusted to reduceundesirable therapy interactions.

The effectiveness of disordered breathing therapy may be assessed bydetecting and analyzing episodes of disordered breathing that occur eventhough therapy is being delivered to mitigate disordered breathing. Asindicated, a number of conditions listed in Table 3 may be used inconnection with the detection of disordered breathing. Methods andsystems for detecting and assessing disordered breathing using one ormore detected patient conditions is described in commonly owned U.S.patent application Ser. No. 10/309,770, filed Dec. 4, 2002, entitled“Detection of Disordered Breathing.”

The flowchart of FIG. 10 illustrates a method of providing disorderedbreathing therapy in accordance with embodiments of the invention. Afirst group of patient conditions is detected 1010 and disorderedbreathing predicted 1020 based on the first group of patient conditions.Therapy to mitigate or prevent the disordered breathing is delivered1030. A second group of conditions is detected 1040 and used to assess1050 the effectiveness of the therapy. The second group of conditionsmay include, for example, conditions used to detect disordered breathingand analyze the type, frequency, duration, and severity of disorderedbreathing episodes. If therapy is ineffective 1060, the therapy regimenmay be adjusted 1070.

One or more conditions of the second group of conditions are used toassess 1060 the impact of the therapy on the patient. If the therapyimpacts the patient negatively, for example, by disrupting sleep orcausing discomfort, then therapy parameters may be adjusted, e.g., toprovide a less aggressive therapy regimen.

According to various embodiments of the invention, disordered breathingdetection may be used to assess therapy effectiveness. In one exampleimplementation, episodes of disordered breathing are detected byanalyzing the patient's respiration. FIG. 11 illustrates a normalrespiration pattern as represented by a transthoracic impedance sensorsignal. The transthoracic impedance increases during respiratoryinspiration and decreases during respiratory expiration. During Non-REMsleep, a normal respiration pattern includes regular, rhythmicinspiration—expiration cycles without substantial interruptions.

In one embodiment, episodes of disordered breathing may be detected bymonitoring the respiratory waveform output of the transthoracicimpedance sensor. When the tidal volume (TV) of the patient'srespiration, as indicated by the transthoracic impedance signal, fallsbelow a hypopnea threshold, then a hypopnea event is declared. Forexample, a hypopnea event may be declared if the patient's tidal volumefalls below about 50% of the recent average tidal volume or otherbaseline tidal volume. When the patient's tidal volume falls further toan apnea threshold, e.g., about 10% of the recent average tidal volume,an apnea event is declared.

In another embodiment, detection of disordered breathing, including, forexample, sleep apnea and hypopnea, involves defining and examining anumber of respiratory cycle intervals. FIG. 12 illustrates respirationintervals used for disordered breathing detection according to anembodiment of the invention. A respiration cycle is divided into aninspiration period corresponding to the patient inhaling, an expirationperiod, corresponding to the patient exhaling, and a non-breathingperiod occurring between inhaling and exhaling. Respiration intervalsare established using inspiration 1210 and expiration 1220 thresholds.The inspiration threshold 1210 marks the beginning of an inspirationperiod 1230 and is determined by the transthoracic impedance signalrising above the inspiration threshold 1210. The inspiration period 1230ends when the transthoracic impedance signal is maximum 1240. A maximumtransthoracic impedance signal 1240 corresponds to both the end of theinspiration interval 1230 and the beginning of the expiration interval1250. The expiration interval 1250 continues until the transthoracicimpedance falls below an expiration threshold 1220. A non-breathinginterval 1260 starts from the end of the expiration period 1250 andcontinues until the beginning of the next inspiration period 1270.

Detection of sleep apnea and severe sleep apnea according to embodimentsof the invention is illustrated in FIG. 13. The patient's respirationsignals are monitored and the respiration cycles are defined accordingto inspiration 1330, expiration 1350, and non-breathing 1360 intervalsas described in connection with FIG. 12. A condition of sleep apnea isdetected when a non-breathing period 1360 exceeds a first predeterminedinterval 1390, denoted the sleep apnea interval. A condition of severesleep apnea is detected when the non-breathing period 1360 exceeds asecond predetermined interval 1395, denoted the severe sleep apneainterval. For example, sleep apnea may be detected when thenon-breathing interval exceeds about 10 seconds, and severe sleep apneamay be detected when the non-breathing interval exceeds about 20seconds.

Hypopnea is a condition of disordered breathing characterized byabnormally shallow breathing. FIGS. 14A-B are graphs of tidal volumederived from transthoracic impedance measurements. The graphs comparethe tidal volume of a normal breathing cycle to the tidal volume of ahypopnea episode. FIG. 14A illustrates normal respiration tidal volumeand rate. As shown in FIG. 14B, hypopnea involves a period of abnormallyshallow respiration.

According to an embodiment of the invention, hypopnea is detected bycomparing a patient's respiratory tidal volume to a hypopnea tidalvolume threshold. The tidal volume for each respiration cycle is derivedfrom transthoracic impedance measurements acquired in the mannerdescribed above. The hypopnea tidal volume threshold may be establishedusing clinical results providing a representative tidal volume andduration of hypopnea events. In one configuration, hypopnea is detectedwhen an average of the patient's respiratory tidal volume taken over aselected time interval falls below the hypopnea tidal volume threshold.Furthermore, various combinations of hypopnea cycles, breath intervals,and non-breathing intervals may be used to detect hypopnea, where thenon-breathing intervals are determined as described above.

FIG. 15 is a flow graph illustrating a method of apnea and/or hypopneadetection according to embodiments of the invention. Various parametersare established 1501 before analyzing the patient's respiration fordisordered breathing episodes, including, for example, inspiration andexpiration thresholds, sleep apnea interval, severe sleep apneainterval, and hypopnea tidal volume threshold.

The patient's transthoracic impedance is measured 1505 as described inmore detail above. If the transthoracic impedance exceeds 1510 theinspiration threshold, the beginning of an inspiration interval isdetected 1515. If the transthoracic impedance remains below 1510 theinspiration threshold, then the impedance signal is checked 1505periodically until inspiration 1515 occurs.

During the inspiration interval, the patient's transthoracic impedanceis monitored until a maximum value of the transthoracic impedance isdetected 1520. Detection of the maximum value signals an end of theinspiration period and a beginning of an expiration period 1535.

The expiration interval is characterized by decreasing transthoracicimpedance. When the transthoracic impedance falls below 1540 theexpiration threshold, a non-breathing interval is detected 1555.

If the transthoracic impedance does not exceed 1560 the inspirationthreshold within a first predetermined interval 1565, denoted the sleepapnea interval, then a condition of sleep apnea is detected 1570. Severesleep apnea is detected 1580 if the non-breathing period extends beyonda second predetermined interval 1575, denoted the severe sleep apneainterval.

When the transthoracic impedance exceeds 1560 the inspiration threshold,the tidal volume from the peak-to-peak transthoracic impedance iscalculated, along with a moving average of past tidal volumes 1585. Thepeak-to-peak transthoracic impedance provides a value proportional tothe tidal volume of the respiration cycle. This value is compared 1590to a hypopnea tidal volume threshold. If the peak-to-peak transthoracicimpedance is consistent with 1590 the hypopnea tidal volume thresholdfor a predetermined time 1592, then a hypopnea cycle is detected 1595.

Additional sensors, such as motion sensors and/or posture sensors, maybe used to confirm or verify the detection of a sleep apnea or hypopneaepisode. The additional sensors may be employed to prevent false ormissed detections of sleep apnea/hypopnea due to posture and/or motionrelated artifacts.

Another embodiment of the invention involves classifying respirationpatterns as disordered breathing episodes based on the breath intervalsand/or tidal volumes of one or more respiration cycles within therespiration patterns. According to this embodiment, the duration andtidal volumes associated with a respiration pattern are compared toduration and tidal volume thresholds. The respiration pattern isdetected as a disordered breathing episode based on the comparison.

According to principles of the invention, a breath interval 1630 isestablished for each respiration cycle. A breath interval represents theinterval of time between successive breaths, as illustrated in FIG. 16.A breath interval 1630 may be defined in a variety of ways, for example,as the interval of time between successive maxima 1610, 1620 of theimpedance signal waveform.

Detection of disordered breathing, in accordance with methods of theinvention, involves the establishment of a duration threshold and atidal volume threshold. If a breath interval exceeds the durationthreshold, an apnea event is detected. Detection of sleep apnea, inaccordance with this embodiment, is illustrated in the graph of FIG. 16.Apnea represents a period of non-breathing. A breath interval 1630exceeding a duration threshold 1640, comprises an apnea episode.

Hypopnea may be detected using the duration threshold and tidal volumethreshold. A hypopnea event represents a period of shallow breathing.Each respiration cycle in a hypopnea event is characterized by a tidalvolume less than the tidal volume threshold. Further, the hypopnea eventinvolves a period of shallow breathing greater than the durationthreshold.

A hypopnea detection approach, in accordance with embodiments of theinvention, is illustrated in FIG. 17. Shallow breathing is detected whenthe tidal volume of one or more breaths is below a tidal volumethreshold 1710. If the shallow breathing continues for an intervalgreater than a duration threshold 1720, then the breathing patternrepresented by the sequence of shallow respiration cycles, is classifiedas a hypopnea event.

FIGS. 18A and 18B provide charts illustrating classification ofindividual disordered breathing events and series of periodicallyrecurring disordered breathing events, respectively. As illustrated inFIG. 18A, individual disordered breathing events may be grouped intoapnea, hypopnea, tachypnea and other disordered breathing events. Apneaevents are characterized by an absence of breathing. Intervals ofreduced respiration are classified as hypopnea events. Tachypnea eventsinclude intervals of rapid respiration characterized by an elevatedrespiration rate.

As illustrated in FIG. 18A, apnea and hypopnea events may be furthersubdivided as either central events, caused either by central nervoussystem dysfunction, or obstructive events, caused by upper airwayobstruction. A tachypnea event may be further classified as a hyperpneaevent, represented by hyperventilation, i.e., rapid deep breathing. Atachypnea event may alternatively be classified as rapid shallowbreathing, typically of prolonged duration.

FIG. 18B illustrates classification of combinations of periodicallyrecurring disordered breathing events. Periodic breathing may beclassified as obstructive, central or mixed. Obstructive periodicbreathing is characterized by cyclic respiratory patterns with anobstructive apnea or hypopnea event in each cycle. In central periodicbreathing, the cyclic respiratory patterns include a central apnea orhypopnea event in each cycle. Periodic breathing may also be of mixedorigin. In this case, cyclic respiratory patterns have a mixture ofobstructive and central apnea events in each cycle. Cheyne-Stokes is aparticular type of periodic breathing characterized by a gradual waxingand waning or tidal volume and having a central apnea and hyperpneaevent in each cycle. Other manifestations of periodic breathing are alsopossible.

As illustrated in FIGS. 18C-G, a respiration pattern detected as adisordered breathing episode may include only an apnea respiration cycle1810 (FIG. 18C), only hypopnea respiration cycles 1850 (FIG. 18F), or amixture of hypopnea and apnea respiration cycles 1820 (FIG. 18D), 1830(FIG. 18E), 1860 (FIG. 18G). A disordered breathing event 1820 may beginwith an apnea respiration cycle and end with one or more hypopneacycles. In another pattern, the disordered breathing event 1830 maybegin with hypopnea cycles and end with an apnea cycle. In yet anotherpattern, a disordered breathing event 1860 may begin and end withhypopnea cycles with an apnea cycle in between the hypopnea cycles.

FIG. 19 is a flow graph of a method for detecting disordered breathingby classifying breathing patterns using breath intervals in conjunctionwith tidal volume and duration thresholds as previously described above.In this example, a duration threshold and a tidal volume threshold areestablished for determining both apnea and hypopnea breath intervals. Anapnea episode is detected if the breath interval exceeds the durationthreshold. A hypopnea episode is detected if the tidal volume ofsuccessive breaths remains less than the tidal volume threshold for aperiod in excess of the duration threshold. Mixed apnea/hypopneaepisodes may also occur. In these cases, the period of disorderedbreathing is characterized by shallow breaths or non-breathingintervals. During the mixed apnea/hypopnea episodes, the tidal volume ofeach breath remains less than the tidal volume threshold for a periodexceeding the duration threshold.

Transthoracic impedance is sensed and used to determine the patient'srespiration cycles. Each breath 1910 is characterized by a breathinterval, defined by the interval of time between two impedance signalmaxima, and a tidal volume (TV).

If a breath interval exceeds 1915 the duration threshold, then therespiration pattern is consistent with an apnea event, and an apneaevent trigger is turned on 1920. If the tidal volume of the breathinterval exceeds 1925 the tidal volume threshold, then the breathingpattern is characterized by two respiration cycles of normal volumeseparated by a non-breathing interval. This pattern represents a purelyapneic disordered breathing event, and apnea is detected 1930. Becausethe final breath of the breath interval was normal, the apnea eventtrigger is turned off 1932, signaling the end of the disorderedbreathing episode. However, if the tidal volume of the breath intervaldoes not exceed 1925 the tidal volume threshold, the disorderedbreathing period is continuing and the next breath is checked 1910.

If the breath interval does not exceed 1915 the duration threshold, thenthe tidal volume of the breath is checked 1935. If the tidal volume doesnot exceed 1935 the tidal volume threshold, the breathing pattern isconsistent with a hypopnea cycle and a hypopnea event trigger is set on1940. If the tidal volume exceeds the tidal volume threshold, then thebreath is normal.

If a period of disordered breathing is in progress, detection of anormal breath signals the end of the disordered breathing. If disorderedbreathing was previously detected 1945, and if the disordered breathingevent duration has not exceeded 1950 the duration threshold, and thecurrent breath is normal, then no disordered breathing event is detected1955. If disordered breathing was previously detected 1945, and if thedisordered breathing event duration has extended for a period of timeexceeding 1950 the duration threshold, and the current breath is normal,then the disordered breathing trigger is turned off 1960. In thissituation, the duration of the disordered breathing episode was ofsufficient duration to be classified as a disordered breathing episode.If an apnea event was previously triggered 1965, then an apnea event isdeclared 1970. If a hypopnea was previously triggered 1965, then ahypopnea event is declared 1975.

FIG. 20 illustrates a block diagram of a system 2000 that may be used toprovide disordered breathing therapy in accordance with embodiments ofthe invention. According to various embodiments, therapy to mitigatedisordered breathing may be triggered by a prediction of disorderedbreathing. The therapy may be adapted based on therapy efficacy and/orthe impact of the therapy on the patient.

In one example implementation, a disordered breathing therapy controlsystem 2010 is incorporated within a cardiac rhythm management system2001 capable of providing therapeutic electrical stimulation to apatient's heart. The cardiac rhythm management system 2001 may include,for example, a cardiac therapy module 2020, including a pacemaker 2022,and an arrhythmia detector/therapy unit 2024. The cardiac rhythmmanagement system 2001 is coupled to a lead system having electrodes2031 electrically coupling the patient's heart 2030 to the cardiacrhythm management system 2001.

The cardiac rhythm management system 2001 may include circuitry 2050used to detect signals from patient-internal sensors such as theimplanted cardiac electrodes 2031, and other patient-internal sensors2080, including, for example, any of the patient-internal sensors listedin Table 1. The cardiac electrodes 2031 and the patient-internal sensors2080 are coupled to a cardiac signal detector 2052 and apatient-internal sensor signal detector 2053, respectively. The cardiacelectrodes 2031 and patient-internal sensors 2080 may be coupled to thedetector system 2050 through conducting leads as shown, or through awireless connection, for example.

The cardiac rhythm management system 2001 may also include circuitry2054 for detecting signals from patient-external sensors 2090 positionedoutside the patient's body. The patient-external sensors 2090 may becoupled to the detection circuitry 2054 through a wireless link. Inaddition, the patient may input information relevant to disorderedbreathing detection or therapy using a patient input device 2070.Signals representing patient-reported data may be wirelessly coupled topatient-input detection circuitry 2055.

The cardiac therapy module 2020 receives cardiac signals from theimplanted cardiac electrodes 2031 and analyzes the cardiac signals todetermine an appropriate cardiac therapy. The cardiac therapy mayinclude pacing therapy controlled by the pacemaker 2022 to treat cardiacrhythms that are too slow. For example, the pacemaker 2022 may controlthe delivery of periodic low energy pacing pulses to one or more of theheart chambers through the cardiac electrodes 2031 to ensure that theperiodic contractions of the heart are maintained at a hemodynamicallysufficient rate.

The cardiac therapy may also include therapy to terminatetachyarrhythmia,

wherein the heart rate is too fast. The arrhythmia detector/therapy unit2024 detects and treats episodes of tachyarrhythmia, includingtachycardia and/or fibrillation. The arrhythmia detector/therapy unit2024 recognizes cardiac signal waveforms indicative of tachyarrhythmiaand controls the delivery of high energy stimulations to the heart 2030through the implanted electrodes 2031 to terminate the arrhythmia.

A disordered breathing control module 2010 incorporated within thecardiac rhythm management system 2001 includes a prediction engine 2015,disordered breathing detection circuitry 2016, and a therapy adaptationcontrol module 2011. The signal detection circuitry 2050 detectsconditions relevant to disordered breathing prediction, detection, andtherapy control used by to the disordered breathing control module 2010.

A prediction of disordered breathing by the disordered breathingprediction engine 2015 may be used to trigger cardiac pacing therapydelivered by the cardiac therapy module 2020 to mitigate or prevent thedisordered breathing. In one illustrative therapy regimen, pacing tomitigate disordered breathing may include pacing at a rate exceeding anintrinsic rate. In another example, the pacing pulses may be deliveredat a rate above the patient's normal sleep rate. The pacing may involveany or all of the heart chambers, for example, right and left atria andright and left ventricles. The pacing may also involve bi-atrial,bi-ventricular, or multi-site pacing. In bi-atrial pacing, the pacingpulses may be delivered to left and right atria simultaneously, oraccording to other timing sequences. Bi-ventricular pacing may beaccomplished by the simultaneous or otherwise timed application ofpacing pulses to the left and right ventricles of the heart.

In other embodiments, adapting the cardiac electrical therapy tomitigate disordered breathing may involve initiating a particular pacingregimen or switching from one pacing mode to another pacing mode. In oneexample, the cardiac pacing regimen may be switched from a dual-chamberpacing mode to a bi-ventricular or other resynchronization mode. Inother examples, the pacing mode may be switched to a pacing mode thatpromotes atrial pacing, or promotes consistent ventricular pacing. Inyet another example, the cardiac electrical therapy may involveinitiating multi-site electrical stimulation to the heart or changingfrom one electrical stimulation site to another. The pacing mode may beswitched from single chamber to multiple chambers, or the reverse. Forexample, a bi-ventricular mode may be switched to a left ventricularmode only. Alternatively, a single chamber mode, e.g., LV or RV, may beswitched to a bi-ventricular mode. Other therapy regimens, involving,e.g., various pacing modes, pacing sites, or non-excitatory electricalstimulations, are possible in connection with providing cardiacelectrical therapy for disordered breathing. The type of cardiacelectrical therapy beneficial to a patient is highly patient specificand may be determined based on the responses of a particular patient.

The patient conditions detected by the signal detector system 2050 areused to adapt disordered breathing therapy with respect to providing amore effective therapy, or to decrease the negative impact of thetherapy on the patient, or both. A therapy adaptation control module2011 coupled to the therapy module includes components for assessingpatient impact 2012 and therapy efficacy 2013 implemented according tothe previously described methods. The therapy adaptation control module2011 is coupled to the therapy control module 2020 and provides controlsignals to the therapy module for adapting the therapy.

FIG. 21 is a flow graph illustrating a method of providing cardiacpacing therapy for disordered breathing in accordance with embodimentsof the invention. The cardiac pacing therapy is triggered by aprediction of disordered breathing. In this example, disorderedbreathing is predicted based on an air pollution index obtained from aninternet accessible server. Therapy efficacy is assessed by analyzingrespiration patterns detected using a transthoracic impedance sensor todetect episodes of disordered breathing. The impact of the therapy onthe patient's sleep is analyzed by determining the number of arousalsper hour experienced by the patient.

As illustrated in FIG. 21, an air pollution index is detected 2105, forexample, by accessing an internet-connected website. If the airpollution index exceeds a selected threshold 2110, then disorderedbreathing is predicted 2115. The air pollution index threshold may beselected, for example, from data collected over time from the patient.If disordered breathing is predicted 2115, therapy to mitigate thedisordered breathing, e.g., cardiac pacing therapy, is delivered 2120 tothe patient. The pacing may involve, for example, pacing at apredetermined amount over the intrinsic rate or the current pacing rate.The predetermined amount may initially be a nominal amount, such as 15bpm over the intrinsic or the current rate. The pacing rate may bemodified to increase the efficacy of the therapy or to reduce the impactof the therapy on the patient.

A transthoracic impedance signal is sensed 2125 and used to analyze 2130respiration patterns associated with disordered breathing. If disorderedbreathing is detected 2135, then the delivered therapy may not have beeneffective 2170. If therapy is found to be ineffective, the therapy maybe adapted 2175.

In one embodiment, if the frequency, duration, or severity of thedisordered breathing episodes is not mitigated following therapydelivery, the therapy may be determined to be ineffective and the pacingrate may be adapted to a higher pacing rate. Severity of disorderedbreathing events may be assessed, for example, as a percentage decreasein tidal volume from the recent average or baseline tidal volume.

Disordered breathing time duration thresholds may be defined to triggeran disordered breathing episode. For example, a disordered breathingepisode may be declared if the patient's tidal volume falls below anapnea or hypopnea tidal volume threshold for a period exceeding adisordered breathing duration threshold such as about 10 seconds. Asevere disordered breathing episode may be declared when the patient'stidal volume falls below an apnea or hypopnea tidal volume threshold fora period exceeding a severe disordered breathing duration threshold,e.g., about 60 seconds. If a severe apnea episode is detected, thesevere apnea episode may trigger pacing at a high rate to arouse thepatient and terminate the apnea. A pacing rate upper limit may beemployed to prevent the pacing rate from becoming too high.

In one embodiment, if the therapy is determined to be effective, thepacing rate may be gradually decreased to reduce the risk of arousal, toavoid unnecessary stress on the heart, and to prolong battery life.

If the disordered breathing therapy is determined to be effective 2135,the impact of the therapy on the patient is assessed. The patient'ssleep quality may be determined by analyzing patient activity using anaccelerometer, for example. Additional sensors may also be used toprovide more sensitive arousal detection. The accelerometer signal issensed 2140 and used to determine 2145 the number of arousals per hour(NH) experienced by the patient. If the number of arousals per hour isgreater 2150 than a threshold value, then the therapy may be arousingthe patient from sleep. In this situation, the impact of the therapy isnot minimal 2165, and the therapy may be adapted 2175. The impact of thetherapy may be further assessed using patient-reported input 2155. Ifthe patient reports that sleep is not restful 2160, then the therapyregimen may be adapted 2175.

Although a number of the examples of disordered breathing therapyprovided above involve types of disordered breathing that generallyoccur while a person is asleep, disordered breathing may also occurwhile a person is awake. While the methods, devices, and systems of theinvention described herein are particularly well-suited for providingsleep-disordered breathing therapy, the principles of the invention arealso applicable to provided therapy for disordered breathing episodesthat occur while the patient is awake. Waking disordered breathing isfrequently associated with compromised cardiopulmonary function causedby congestive heart failure. Examples of the types of disorderedbreathing that may occur while a person is awake include, for example,periodic breathing and Cheyne-Stokes respiration. Cheyne-Stokesrespiration particularly affects patients who have heart problems, suchas congestive heart failure, or nervous disorders, such as those causedby a stroke.

The following commonly owned U.S. patents applications, some of whichhave been identified above, are hereby incorporated by reference intheir respective entireties: U.S. patent application Ser. No. 10/309,771filed Dec. 4, 2002, now U.S. Pat. No. 7,189,204, U.S. patent applicationSer. No. 10/309,770 filed Dec. 4, 2002, now U.S. Pat. No. 7,252,640,U.S. patent application Ser. No. 10/642,998 entitled “Sleep Quality DataCollection and Evaluation,” now U.S. Publication No. 2005/0042589, andconcurrently filed with this patent application, U.S. patent applicationSer. No. 10/643,203 entitled “Adaptive Therapy for DisorderedBreathing,” now U.S. Publication No. 2005/0039745 and filed concurrentlywith this patent application, U.S. patent application Ser. No.10/643,006 entitled “Sleep State Classification,” now U.S. PublicationNo. 2005/0043652, and filed concurrently with this patent application,and U.S. patent application Ser. No. 10/643,016 entitled “Prediction ofDisordered Breathing,” now U.S. Pat. No. 7,396,333, and filedconcurrently with this patent application.

Various modifications and additions can be made to the preferredembodiments discussed hereinabove without departing from the scope ofthe present invention. Accordingly, the scope of the present inventionshould not be limited by the particular embodiments described above, butshould be defined only by the claims set forth below and equivalentsthereof.

Various modifications and additions can be made to the preferredembodiments discussed hereinabove without departing from the scope ofthe present invention. Accordingly, the scope of the present inventionshould not be limited by the particular embodiments described above, butshould be defined only by the claims set forth below and equivalentsthereof.

1. A method of providing disordered breathing therapy to a patient, comprising: detecting via a detector system one or more conditions associated with disordered breathing of a patient; predicting disordered breathing based on the one or more detected conditions using at least a first disordered breathing prediction criteria set; estimating an accuracy of the first disordered breathing prediction criteria set; adapting a therapy for the patient to mitigate the predicted disordered breathing; and delivering the adapted therapy to the patient via a therapy delivery system, wherein at least one of predicting, adapting, and delivering is performed at least in part implantably.
 2. The method of claim 1, wherein each of predicting, adapting, and delivering is performed at least in part implantably.
 3. The method of claim 1, further comprising: using a first group of the one or more detected conditions for predicting disordered breathing; and using a second group of the detected conditions for adapting the therapy to mitigate the predicted disordered breathing.
 4. The method of claim 3, wherein the one or more detected conditions comprises any combination of a physiological condition and a contextual condition.
 5. The method of claim 3, wherein the second group of the detected conditions comprises one or more of: a condition used to assess an impact of the therapy on the patient a condition used to assess therapy efficacy, a condition associated with sleep quality, a condition associated with patient comfort, a condition used to assess respiration quality, a cardiovascular system condition, a respiratory system condition, a nervous system condition, a blood chemistry condition, and a muscle system condition.
 6. The method of claim 1, wherein adapting the therapy comprises adapting the therapy based on one or more of patient therapy interactions and detection of one or more episodes of disordered breathing.
 7. The method of claim 6, wherein the detection of the one or more episodes of disordered breathing comprises detecting any combination of a severity, a duration, and a frequency of the one or more detected episodes of disordered breathing.
 8. The method of claim 1, wherein adapting the therapy comprises adapting the therapy to perform any combination of terminating and preventing the predicted disordered breathing.
 9. The method of claim 1, wherein adapting the therapy comprises adapting a cardiac pacing therapy.
 10. The method of claim 9, wherein adapting the cardiac pacing therapy comprises adapting any combination of atrial pacing therapy, ventricular pacing therapy, multi-chamber pacing therapy.
 11. The method of claim 9, wherein adapting the cardiac pacing therapy comprises any combination of adapting the cardiac pacing therapy at a rate above an intrinsic rate, adapting the cardiac pacing therapy at a rate above a normally programmed pacing rate, switching pacing sites, and switching pacing modes.
 12. A medical device, comprising: a detector system configured to detect one or more conditions associated with disordered breathing of a patient; a prediction engine coupled to the detector system and configured to: predict disordered breathing based on the detected conditions using at least a first disordered breathing prediction criteria set; estimate an accuracy of the first disordered breathing prediction criteria set; and adapt a therapy for the patient to mitigate the predicted disordered breathing; and a therapy delivery system coupled to the prediction engine and the detector system and configured to deliver the adapted therapy to the patient, wherein the prediction engine includes an implantable component.
 13. The medical device of claim 12, wherein one or both of the therapy delivery system and the detector system include implantable components.
 14. The medical device of claim 12, wherein the prediction engine is further configured to: use a first group of the one or more detected conditions for predicting disordered breathing; and use a second group of the detected conditions for adapting the therapy to mitigate the predicted disordered breathing.
 15. The medical device of claim 14, wherein the one or more conditions comprise any combination of a physiological condition and a contextual condition.
 16. The medical device of claim 14, wherein the second group of the detected conditions comprises one or more of: a condition used to assess an impact of the therapy on the patient a condition used to assess therapy efficacy, a condition associated with sleep quality, a condition associated with patient comfort, a condition used to assess respiration quality, a cardiovascular system condition, a respiratory system condition, a nervous system condition, a blood chemistry condition, and a muscle system condition.
 17. The medical device of claim 12, wherein adapting the therapy comprises adapting the therapy based on one or more of patient therapy interactions and detection of one or more episodes of disordered breathing.
 18. The medical device of claim 12, wherein adapting the therapy comprises adapting the therapy to perform any combination of terminating and preventing the predicted disordered breathing.
 19. The medical device of claim 12, wherein adapting the therapy comprises adapting a cardiac pacing therapy.
 20. The medical device of claim 12, wherein the prediction engine is configured to perform real-time prediction of the disordered breathing. 